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Understanding Physiology of Diseases and Cell Lines Using Omics Based Approaches

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Understanding Physiology of Diseases and Cell Lines Using Omics Based Approaches UNDERSTANDING PHYSIOLOGY OF DISEASES AND CELL LINES USING OMICS BASED APPROACHES by Amit Kumar A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland May, 2015 © 2015 Amit Kumar All Rights Reserved Abstract This thesis focuses on understanding physiology of diseases and cell lines using OMICS based approaches such as microarrays based gene expression analysis and mass spectrometry based proteins analysis. It includes extensive work on functionally characterizing mass spectrometry based proteomics data for identifying secreted proteins using bioinformatics tools. This dissertation also includes work on using omics based techniques coupled with bioinformatics tools to elucidate pathophysiology of diseases such as Type 2 Diabetes (T2D). Although the well-known characteristic of T2D is hyperglycemia, there are multiple other metabolic abnormalities that occur in T2D, including insulin resistance and dyslipidemia. In order to attain a greater understanding of the alterations in metabolic tissues associated with T2D, microarray analysis of gene expression in metabolic tissues from a mouse model of pre-diabetes and T2D to understand the metabolic abnormalities that may contribute to T2D was performed. This study also uncovered the novel genes and pathways regulated by the insulin sensitizing agent (CL-316,243) to identify key pathways and target genes in metabolic tissues that can reverse the diabetic phenotype. Specifically, he found significant decreases in the expression of mitochondrial and peroxisomal fatty acid oxidation genes in the skeletal muscle and adipose tissue of adult MKR mice, and in the liver of pre-diabetic MKR mice, compared to healthy mice. In addition, this study also explained the lower free fatty acid levels in MKR mice after treatment with CL-316,243 and provided biomarker genes such as ACAA1 and HSD17b4. ii Using results from T2D microarrays studies, a multi-tissue computational model was created using metabolic reconstructions for in silico simulation of T2D for a better understanding of the disease pathophysiology. A time-efficient algorithm for generating tissue-specific metabolic models was presented in this study. The flux balance analysis using the multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. The T2D multi-tissue model was able to explain the high level of branched-chain amino acids and free fatty acids in plasma of T2D subjects from a systems level metabolic fluxes perspective. In addition to T2D studies, this dissertation also reports identification of the complete collection of proteins which make up the Chinese hamster ovary (CHO) cells proteome which has been an invaluable source of information for scientists, allowing them to engineer their cell lines to increase the efficiency of therapeutics production. Proteomics has been especially attractive for biotechnology applications since it can provide an understanding of disease states and aid drug discovery and development. Moreover, CHO cells are the preferred host cell line for manufacturing a variety of biologicals including monoclonal antibodies. A proteomics and bioinformatics analysis on the spent medium from CHO cells was performed. From the analysis of supernatant of post-centrifugation CHO cells, identification of thousands of unique proteins that are potentially secreted from the CHO cells was done. In order to categorize these proteins functionally, multiple bioinformatics tools including SignalP, TargetP, SecretomeP, TMHMM, WoLF PSORT, and Phobius were implemented. This analysis provided information on the cellular localization of the proteins found in the supernatant, including the presence of iii transmembrane domains and signal peptides. Proteins were shown to be localized to the secretory pathway, including ones playing role in cell growth, proliferation, and folding as well as those involved in degradation and removal of other proteins. As a part of this effort, a publically accessible web-based tool called GO-CHO (http://ebdrup.biosustain.dtu.dk/gocho/) was created to functionally categorize the proteins. This work and database will enable the CHO community to rapidly identify high abundance host cell proteins in their cultures in order to facilitate processing and purification efforts in the future. Moreover, the compartmentalization strategies presented in this work will help the CHO community in understanding CHO secretory machinery. Advisors: Michael J. Betenbaugh and Joseph Shiloach iv Preface Emergence of OMICS technologies in recent years have allowed us relatively faster analysis of complex physiology of biological systems including diseases and cell lines displaying the main advantage of obtaining a big amount of information at a relatively low cost and effort and converting it to biologically meaningful results. To analyze cells or tissues by an OMICS approach, various biochemical technologies are employed such as genomics, transcriptomics, proteomics, metabolomics, and so on. Genomics uses technologies such as fluorescence in situ hybridization, comparative genome hybridization arrays, and single nucleotide polymorphism arrays to decipher physiology of biological systems. On the other hand, transcriptomics used mRNA microarrays and real time polymerase chain reaction (RT-PCR) to achieve similar goals. Proteomics technologies include separation techniques such as one-dimensional sodium dodecyl-sulfate polyacrylamide gel (1D-SDS-PAGE), two dimensional (2D) PAGE, high pressure liquid chromatography (HPLC), and ultra-pressure liquid chromatography (UPLC), reverse-phase liquid chromatography tandem mass spectrometry (RP-LC- MS/MS), protein arrays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry, and bioinformatics method to study biological systems. On the other hand, metabolomics employs techniques such as gas chromatography – mass spectrometry (GC-MS), liquid chromatography – mass spectrometry (LC-MS), HPLC, and H nuclear magnetic resonance (H-NMR) for the purpose of studying the events and interactions of cellular structures and process from DNA and genes to metabolites in a complex and global way. Using OMICS platforms, all classes of biological compounds, epigenetic markers, genes, messenger ribonucleic acid (mRNA), proteins and metabolites can be v analyzed. In other words, it can be said that genomics and transcriptomics methods enable assessment of genetic information, proteomics permits assessing actually translated proteins, and metabolomics displays the results after the above three plans are executed. T2D is a complex disease with epidemic proportions and is a public health, economic, scientific issue, and ethical issue and requires proactive and preventive approaches to the individual and public health burden caused by diabetes and its co-morbidities. The complexity of the T2D phenotype has challenged the fragmented scientific approaches, typically focusing on either genetic, or environmental (diet, lifestyle), or socio-economic conditions in isolation rather than on multi-scale, longitudinal, systems-level studies. The focus of this dissertation is to present an emerging novel strategy of utilization of computational methods to study pathophysiology of Type 2 Diabetes (T2D). In addition, OMICS technologies were implemented in studying physiology of Chinese Hamster Ovary (CHO) cells and E. coli. This dissertation consists of six chapters and is mainly focused on implementing OMICS technologies such as transcriptomics and proteomics on improving the understanding of physiology of T2D and cell lines such as CHO cells and E. coli. The first chapter, which was published in PLoS One journal (PMID: 25029527), introduces a computational strategy based on metabolic reconstruction to study metabolic fluxes in T2D condition. The second chapter, which was published in Nutrition and Metabolism journal (PMID: 25784953), discusses effect of T2D in terms of differences in genes expression using microarrays and discusses effect of a drug (CL 316,243) on T2D. In this process of vi studying the effect of T2D and the aforementioned drug, we have also characterized the metabolic characteristics of a T2D animal model – MKR mice. Third chapter, which was published in Proteomics Clinical Applications journal (Reuse license number: 3632651416772), discusses proteomics and its application in understanding physiology of cell lines and diseases. Chapter four discusses transcriptomics and proteomics application in deciphering differences in two strains of E. coli. Fifth chapter, pubished in Pharmaceutical Bioprocessing, is dedicated to advances in proteomics technology specifically related to CHO cells. Chapter six discusses application of proteomics in identifying secreted proteins in CHO cells along with introducing novel bioinformatics strategies. Finally, chapter 7 concludes the dissertation and discusses the future work to extend the efforts presented in this study. Acknowledgements I believe that getting a PhD is a process of evolution, development, learning, and growth. I owe this progress to my family, especially my parents and my wife – Olivia Franken, for their continuous support in my journey to pursue a Doctoral degree. I am deeply thankful to my parents for instilling the enthusiasm in me towards pursuing higher education. I would also like to express my sincere gratitude
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